Effectiveness of Retention Harvesting for Biodiversity Conservation: Evidence for Understory Vegetation and Wildlife
Notice bibliographique
Résumé
Natural disturbance emulation is being used in forest management in an attempt to mitigate the negative effects of harvesting on biodiversity. In the western Canadian boreal forest where the predominant large-scale natural disturbance is wildfire, harvests that leave live mature trees behind at harvest retain some of the structural complexity characteristic of pyrogenic landscapes. Such ‘retention harvests’ are thought to emulate wildfire in a way that conserves biodiversity. Trees can be retained at different levels (percentage of initial basal area retained) and in different patterns (dispersed and aggregated). Although retention harvesting provides for greater structural diversity post-harvest, the forest floor is not burned as it is by wildfire. Therefore, prescribed burning may emulate the influence of wildfires more effectively than does retention harvesting alone. In this dissertation, I explored the effects of different retention levels and patterns, as well as post-harvest prescribed fire, on wildlife and understory vascular plants in the boreal mixedwood forests of northwestern Alberta, Canada. First, I used a combination of midden counts, scat surveys, and camera trapping to compare wildlife use of different stands harvested across a range of dispersed retention levels (0%, 10%, 20%, 50%, 75%, 100%) 15-18 years post-harvest. Second, I examined the effectiveness of combining two retention patch sizes (0.20 ha and 0.46 ha) with different dispersed retention levels (0%, 10%, 20%, 50%, 75%) for supporting understory vascular plant communities that are characteristic of unharvested forest 15 years post-harvest. Third, I investigated the effects of prescribed fire in post-retention harvested stands on understory vascular plant communities up to 12 years post-fire in three different forest cover types (conifer-dominated, mixedwood, deciduous-dominated). Use of harvest stands for late-seral wildlife species was highest in stands having higher retention levels (≥ 50% retention) characterized by high tree basal area and canopy cover. In contrast, lower retention levels (≤ 20% retention) having greater understory cover benefitted early-seral wildlife species. For vascular plants, different retention patch sizes supported distinct understory plant communities with both patch sizes being more effective at supporting late-seral plant communities when surrounded by higher levels of dispersed retention. Prescribed fire benefitted some fire-specialist plant species with the effects of prescribed fire on understory plant communities still evident more than a decade later. Overall, these results suggest that a variety of retention levels, combinations of retention patterns, and the appropriate application of prescribed fire would maintain the structural heterogeneity that supports a wide spectrum of species’ habitats within harvested landscapes. These findings contribute to our understanding, development, and application of effective harvesting practices for sustainable forest management and biodiversity conservation.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».